Are results from pharmaceutical-company-sponsored studies available to the public?

Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study

BACKGROUND

Because vital details of potential pharmacokinetic drug-drug interactions are often described in free-text structured product labels, manual curation is a necessary but expensive step in the development of electronic drug-drug interaction information resources. The use of nonexperts to annotate potential drug-drug interaction (PDDI) mentions in drug product label annotation may be a means of lessening the burden of manual curation.

OBJECTIVE

Our goal was to explore the practicality of using nonexpert participants to annotate drug-drug interaction descriptions from structured product labels. By presenting annotation tasks to both pharmacy experts and relatively naïve participants, we hoped to demonstrate the feasibility of using nonexpert annotators for drug-drug information annotation. We were also interested in exploring whether and to what extent natural language processing (NLP) preannotation helped improve task completion time, accuracy, and subjective satisfaction.

METHODS

Two experts and 4 nonexperts were asked to annotate 208 structured product label sections under 4 conditions completed sequentially: (1) no NLP assistance, (2) preannotation of drug mentions, (3) preannotation of drug mentions and PDDIs, and (4) a repeat of the no-annotation condition. Results were evaluated within the 2 groups and relative to an existing gold standard. Participants were asked to provide reports on the time required to complete tasks and their perceptions of task difficulty.

RESULTS

One of the experts and 3 of the nonexperts completed all tasks. Annotation results from the nonexpert group were relatively strong in every scenario and better than the performance of the NLP pipeline. The expert and 2 of the nonexperts were able to complete most tasks in less than 3 hours. Usability perceptions were generally positive (3.67 for expert, mean of 3.33 for nonexperts).

CONCLUSIONS

The results suggest that nonexpert annotation might be a feasible option for comprehensive labeling of annotated PDDIs across a broader range of drug product labels. Preannotation of drug mentions may ease the annotation task. However, preannotation of PDDIs, as operationalized in this study, presented the participants with difficulties. Future work should test if these issues can be addressed by the use of better performing NLP and a different approach to presenting the PDDI preannotations to users during the annotation workflow.

Why are medical and health-related studies not being published? A systematic review of reasons given by investigators

Objective

About half of medical and health-related studies are not published. We conducted a systematic review of reports on reasons given by investigators for not publishing their studies in peer-reviewed journals.

Methods

MEDLINE, EMBASE, PsycINFO, and SCOPUS (until 13/09/2013), and references of identified articles were searched to identify reports of surveys that provided data on reasons given by investigators for not publishing studies. The proportion of non-submission and reasons for non-publication was calculated using the number of unpublished studies as the denominator. Because of heterogeneity across studies, quantitative pooling was not conducted. Exploratory subgroup analyses were conducted.

Results

We included 54 survey reports. Data from 38 included reports were available to estimate proportions of at least one reason given for not publishing studies. The proportion of non-submission among unpublished studies ranged from 55% to 100%, with a median of 85%. The reasons given by investigators for not publishing their studies included: lack of time or low priority (median 33%), studies being incomplete (median 15%), study not for publication (median 14%), manuscript in preparation or under review (median 12%), unimportant or negative result (median 12%), poor study quality or design (median 11%), fear of rejection (median 12%), rejection by journals (median 6%), author or co-author problems (median 10%), and sponsor or funder problems (median 9%). In general, the frequency of reasons given for non-publication was not associated with the source of unpublished studies, study design, or time when a survey was conducted.

Conclusions

Non-submission of studies for publication remains the main cause of non-publication of studies. Measures to reduce non-publication of studies and alternative models of research dissemination need to be developed to address the main reasons given by investigators for not publishing their studies, such as lack of time or low priority and fear of being rejected by journals.

Time to publication for NIHR HTA programme-funded research: a cohort study

OBJECTIVE

To assess the time to publication of primary research and evidence syntheses funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme published as a monograph in Health Technology Assessment and as a journal article in the wider biomedical literature.

STUDY DESIGN

Retrospective cohort study.

SETTING

Primary research and evidence synthesis projects funded by the HTA Programme were included in the cohort if they were registered in the NIHR research programmes database and was planned to submit the draft final report for publication in Health Technology Assessment on or before 9 December 2011.

MAIN OUTCOME MEASURES

The median time to publication and publication at 30 months in Health Technology Assessment and in an external journal were determined by searching the NIHR research programmes database and HTA Programme website.

RESULTS

Of 458 included projects, 184 (40.2%) were primary research projects and 274 (59.8%) were evidence syntheses. A total of 155 primary research projects had a completion date; the median time to publication was 23 months (26.5 and 35.5 months to publish a monograph and to publish in an external journal, respectively) and 69% were published within 30 months. The median time to publication of HTA-funded trials (n=126) was 24 months and 67.5% were published within 30 months. Among the evidence syntheses with a protocol online date (n=223), the median time to publication was 25.5 months (28 months to publication as a monograph), but only 44.4% of evidence synthesis projects were published in an external journal. 65% of evidence synthesis studies had been published within 30.0 months.

CONCLUSIONS

Research funded by the HTA Programme publishes promptly. The importance of Health Technology Assessment was highlighted as the median time to publication was 9 months shorter for a monograph than an external journal article.

Inhibitory metabolic drug interactions with newer psychotropic drugs: inclusion in package inserts and influences of concurrence in drug interaction screening software

BACKGROUND

Food and Drug Administration (FDA) regulations mandate that package inserts (PIs) include observed or predicted clinically significant drug-drug interactions (DDIs), as well as the results of pharmacokinetic studies that establish the absence of effect.

OBJECTIVE

To quantify how frequently observed metabolic inhibition DDIs affecting US-marketed psychotropics are present in FDA-approved PIs and what influence the source of DDI information has on agreement between 3 DDI screening programs.

METHODS

The scientific literature and PIs were reviewed to determine all drug pairs for which there was rigorous evidence of a metabolic inhibition interaction or noninteraction. The DDIs were tabulated noting the source of evidence and the strength of agreement over chance. Descriptive statistics were used to examine the influence of source of DDI information on agreement among 3 DDI screening tools. Logistic regression was used to assess the influence of drug class, indication, generic status, regulatory approval date, and magnitude of effect on agreement between the literature and PI as well as agreement among the DDI screening tools.

RESULTS

Thirty percent (13/44) of the metabolic inhibition DDIs affecting newer psychotropics were not mentioned in PIs. Drug class, indication, regulatory approval date, generic status, or magnitude of effect did not appear to be associated with more complete DDI information in PIs. DDIs found exclusively in PIs were 3.25 times more likely to be agreed upon by all 3 DDI screening tools than were those found exclusively in the literature. Generic status was inversely associated with agreement among the DDI screening tools (odds ratio 0.11; 95% CI 0.01 to 0.89).

CONCLUSIONS

The presence in PIs of DDI information for newer psychotropics appears to have a strong influence on agreement among DDI screening tools. Users of DDI screening software should consult more than 1 source when considering interactions involving generic psychotropics.